ASPN Is a Potential Biomarker and Associated with Immune Infiltration in Endometriosis

Abstract

Objective: Endometriosis is a benign gynecological disease characterized by distant metastasis. Previous studies have discovered abnormal numbers and function of immune cells in endometriotic lesions. We aimed to find potential biomarkers of endometriosis and to explore the relationship between ASPN and the immune microenvironment of endometriosis. Methods: We obtained the GSE141549 and GSE7305 datasets containing endometriosis and normal endometrial samples from the Gene Expression Omnibus database (GEO). In the GSE141549 dataset, differentially expressed genes (DEGs) were found. The Least Absolute Shrinkage and Selection Operator (Lasso) regression and generalized linear models (GLMs) were used to screen new biomarkers. The expression levels and diagnostic utility of biomarkers were assessed in GSE7305, and biomarker expression levels were further validated using qRT-PCR and western blot. We identified DEGs between high and low expression groups of key biomarkers. Enrichment analysis was carried out to discover the target gene’s biological function. We analyzed the relationship between key biomarker expression and patient clinical features. Finally, the immune cells that infiltrate endometriosis were assessed using the Microenvironment Cell Population-Counter (MCP-counter), and the correlation of biomarker expression with immune cell infiltration and immune checkpoints genes was studied. Results: There were a total of 38 DEGs discovered. Two machine learning techniques were used to identify 10 genes. Six biomarkers (SCG2, ASPN, SLIT2, GEM, EGR1, and FOS) had good diagnostic efficiency (AUC > 0.7) by internal and external validation. We excluded previously reported related genes (SLIT2, EGR1, and FOS). ASPN was the most significantly differentially expressed biomarker between normal and ectopic endometrial tissues, as verified by qPCR. The western blot assay revealed a significant upregulation of ASPN expression in endometriotic tissues. The investigation for DEGs in the ASPN high- and low-expression groups revealed that the DEGs were particularly enriched in extracellular matrix tissue, vascular smooth muscle contraction, cytokine interactions, the calcium signaling pathway, and the chemokine signaling pathway. High ASPN expression was related to r-AFS stage (p = 0.006), age (p = 0.03), and lesion location (p < 0.001). Univariate and multivariate logistic regression analysis showed that ASPN expression was an independent influencing factor in patients with endometriosis. Immune cell infiltration analysis revealed a significant increase in T-cell, B-cell, and fibroblast infiltration in endometriosis lesions; cytotoxic lymphocyte, NK-cell, and endothelial cell infiltration were reduced. Additionally, the percentage of T cells, B cells, fibroblasts, and endothelial cells was favorably connected with ASPN expression, while the percentage of cytotoxic lymphocytes and NK cells was negatively correlated. Immune checkpoint gene (CTLA4, LAG3, CD27, CD40, and ICOS) expression and ASPN expression were positively associated. Conclusions: Increased expression of ASPN is associated with immune infiltration in endometriosis, and ASPN can be used as a diagnostic biomarker as well as a potential immunotherapeutic target in endometriosis.

Peritoneal immune microenvironment of endometriosis: Role and therapeutic perspectives

“…A recent study proposed that active-screen plasma nitriding (ASPN) can be used as a diagnostic biomarker and a potential therapeutic target in endometriosis, which is negatively correlated with the percentage of cytotoxic lymphocytes and NK cells and positively correlated with the percentage of T cells, B cells, fibroblasts, and endothelial cells by the Least Absolute Shrinkage and Selection Operator (LASSO) regression and generalized linear models (GLMs) from the GSE141549 datasets ( 167 ). Notably, TLR-2, which is essential in the innate immune system, was upregulated in the myeloid dendritic cells and B cells in patients with endometriosis.…”

Identifying immune cell infiltration and diagnostic biomarkers for nasopharyngeal carcinoma through bioinformatic analysis

“…SVM-RFE integrates the support vector machine and the recursive feature elimination methodology, using the support vector technology's built-in feature selection function to lter crucial features over continuous iterations. Xiang [8] validated biomarkers for hepatocellular carcinoma prognosis using machine learning; Wang [9] con rmed Adenomyosis biomarkers through LASSO and SVM algorithms. However, there has been limited research on the application of machine -learning approaches in the treatment of nasopharyngeal cancer.…”